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This paper proposes adaptive estimation and control methods for vehicle semi-active suspension systems with magneto-rheological (MR) damper. To incorporate MR damper into the control design, a hyperbolic model is adopted to describe its dynamics, and then adaptive parameter estimation is firstly studied to estimate the unknown parameters of the hyperbolic model. This estimation method requires the...
This paper proposes adaptive control for vehicle active suspensions with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise damper dynamics). An adaptive control is designed to stabilize the altitude of vehicles and to improve the ride comfort, where an augmented neural network is developed to provide the online compensation for the unknown dynamics. A novel adaptive law is proposed...
A novel two step adaptive identification framework is proposed for sinusoidal signals to estimate the unknown offset, amplitude, frequency and phase, where only the output measurements are used. After representing the sinusoidal signal as a linearly parameterized form, several adaptive laws are developed. The proposed adaptive laws are driven by parameter estimation error information that is derived...
This paper presents a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems, which can guarantee the prescribed error convergence performance (e.g. overshoot, convergence rate). By introducing appropriate filter operations, an explicit expression of parameter estimation error is obtained. Then a prescribed performance function (PPF) and the associate transform...
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